ByteBot Open-Source AI Desktop Agent

Introduction

AI is finally moving onto the desktop. ByteBot is an open-source AI desktop agent that can control a virtual desktop to automate real computer tasks. In this guide, I install it locally, explain how it works, set it up with Docker, and run real tasks to show its capabilities and limits.

ByteBot operates through natural language instructions. You type what you want done, and it performs actions by clicking, typing, and navigating across apps, just like a human. It supports browsers, email clients, office tools, and can work with PDFs, spreadsheets, and workflows that span multiple applications.

I’ll walk through installation, model setup, architecture, deployment options, and a quick tour of the interface. I’ll also cover privacy, security, and cost considerations so you can decide if it suits your environment.

What is ByteBot?

ByteBot is an AI agent that runs on a self-hosted server and controls a virtual desktop environment to complete tasks. It uses large language models (LLMs) to reason about UI elements, handle pop-ups, and adapt to changing interfaces. You can keep an eye on what it’s doing, step in as needed, or let it run in autonomous mode.

It’s designed for repetitive workflows—data entry, form filling, and multi-system operations—without scripting. It is built to be deployed locally and can be integrated with enterprise workflows through containerized infrastructure.

Table Overview: ByteBot at a Glance

Item	Summary
Purpose	Automates desktop tasks by controlling a virtual desktop through natural language instructions
License	Open-source
Interfaces	Browsers, email clients, office tools; handles PDFs and spreadsheets
Core actions	Clicking, typing, navigating UIs, opening apps, reading screens, running terminal commands
Typical tasks	Data entry, form filling, cross-application workflows, basic web research
Privacy model	Runs on self-hosted servers; manual intervention possible; autonomous and takeover modes
Models supported	OpenAI, Gemini, Anthropic (API keys required)
Local models	Not supported in the current build; Ollama support is not available as of now
Deployment	Docker (local), Kubernetes via Helm charts, Railway (with API key)
Protocols	Model Context Protocol (MCP) for standardized access to external tools
Data store	Uses Postgres and other services via Docker Compose
Costs	Depends on LLM API usage; monitor closely for web-heavy tasks
Desktop access	Can expose a direct desktop feed for monitoring and control
Platform tested	Ubuntu (Linux) in this walkthrough

Key Features of ByteBot

Natural language tasks: describe the goal; ByteBot plans and executes steps.
Virtual desktop control: simulates user actions—clicks, typing, navigation.
Multi-app workflows: works across browsers, email, and office tools.
Document handling: opens and processes PDFs and spreadsheets.
Autonomy modes: run fully autonomous or allow user takeover mid-run.
Self-hosted: you control the server and desktop environment.
MCP support: connects to external tools through a standardized protocol.
Deployment flexibility: Docker locally, Kubernetes via Helm, or Railway.

Installation and Setup

Prerequisites

A Linux machine (I used Ubuntu).
Docker installed (a recent version).
An API key from a supported provider (OpenAI, Anthropic, or Gemini). Free credits may be available for Gemini, but all are ultimately paid services.

Note: ByteBot currently requires API-based models. Local LLMs via Ollama are not supported at this time.

Clone and Configure

Clone the repository:
- Use git to pull the ByteBot repo to your machine.
Configure your LLM API key:
- Export the key or pass it to Docker as an environment variable.
- You only need to set the key for the provider you’re using. In my case, I used an OpenAI API key.
Verify Docker is ready:
- Ensure you can run docker compose commands without errors.

Start the Services

Bring up the stack:
- Start with Docker Compose. The first run will download containers and dependencies.
- You’ll see services like Postgres and others being pulled.
Wait for “running” status:
- Once containers are healthy, note the local address and port from the logs.
Open the web app:
- Go to the local address in your browser. You should see the ByteBot interface.

How ByteBot Works (Architecture)

Virtual Desktop and Agent

ByteBot runs a virtual desktop that the agent controls step-by-step. It “sees” the screen, moves the cursor, clicks, types, and navigates menus. This approach means it can adapt to visual changes and dynamic content across apps.

Because it acts visually, it can handle unexpected issues like pop-ups or minor UI shifts. That makes it suitable for tasks that are too brittle for traditional rule-based tools.

Privacy, Security, and Control

ByteBot runs on servers you control. You can grant direct desktop access for monitoring or manual intervention. It supports autonomous operation modes and “takeover” options so you can step in.

Important: it still sends prompts and content to external LLM APIs. Review your data handling policies before using it on sensitive material. Understand what may be transmitted, and plan network and access controls accordingly.

Deployment Options and Protocol Support

Docker locally for personal or team testing.
Kubernetes via Helm charts for larger deployments.
Railway with an API key for hosted convenience.
MCP support allows standardized connections to external tools.

First Run and Interface

Accessing the App

After the containers start, open ByteBot in your browser at the local address shown in the logs. If your model provider keys are missing, the interface will warn you.

You’ll land on the main dashboard where you can submit tasks, review histories, and manage settings.

Model Selection and Desktop Access

Set your preferred model under settings. The interface may show options like GPT-4.1 if your provider supports them. You can enable direct desktop access so you can see what the agent is doing and take control if needed.

The desktop view displays live screenshots. You’ll also see recent tasks, logs, and status indicators.

Submitting a Task

Enter a clear natural language instruction describing the goal and any constraints. ByteBot interprets the request, opens apps, and proceeds step-by-step. You’ll see it click, type, and scroll in the desktop viewer.

You can monitor each action with its on-screen coordinates and intermediate screenshots. For long tasks or web scraping, execution can take time.

Walkthrough: Real Tasks I Ran

Flight Search Task

I asked ByteBot to research one-way flights from Sydney to Jakarta in October 2025 and return the cheapest direct option. It used my OpenAI key, opened Firefox on the virtual desktop, and began searching.

During the run, I watched it operate the browser and log each click location. It took a bit to load pages and gather results. The output indicated a direct one-way flight in the ~$370 range. Results appeared in the interface, and I could review the completed task from the dashboard.

Keep an eye on API usage for tasks like this. Web-heavy research can trigger many LLM calls, which can add up quickly.

System Info Task

Next, I asked ByteBot for CPU specs on my system. It opened a terminal and ran the lscpu command, then captured and summarized the output. The data matched my machine.

This shows that ByteBot can go beyond the browser and interact with the local environment—opening terminals, running commands, and reading results. Treat this capability with care and ensure you understand the security implications in your setup.

Practical Notes and Limitations

Model Requirements and Local Models

ByteBot currently runs with API-based LLMs from OpenAI, Anthropic, or Gemini. There’s no option to run with local models right now. Given how it manages screen understanding and complex planning, I don’t expect Ollama-based support in the near term, though it could be a future direction.

API Costs and Rate Limits

Each action can trigger LLM calls. Complex tasks—especially anything involving multiple pages, forms, or scraping—can incur noticeable cost. Monitor usage, set budgets, and consider prompt strategies that keep context focused.

Performance Observations

The agent moves methodically through steps. For tasks like browsing and form interactions, it may feel slow compared to a human. That’s normal for this class of tool. Expect delays for page loads, UI rendering, and model thinking time.

Use Cases

Repetitive data entry across web portals or internal tools.
Form filling and cross-application workflows.
Document handling: opening PDFs, extracting key fields, and moving data into spreadsheets.
Light web research with structured outputs.
Basic system checks or scripted terminal tasks triggered by natural language.

Step-by-Step Setup Summary

Confirm Docker is installed and working.
Clone the ByteBot repository.
Set your model provider API key as an environment variable passed to Docker.
Start the services with Docker Compose; the stack includes Postgres and related components.
Wait for containers to become healthy.
Open the app in your browser at the local address reported by the logs.
Select your model in settings and, if desired, enable desktop access.
Submit a natural language task and monitor the desktop viewer.

Tips for Safe Use

Review data exposure: prompts and content sent to LLM APIs may include sensitive information. Mask or redact where possible.
Use isolated environments for testing: separate networks, limited credentials, and minimal data access.
Set role-based access: restrict who can submit tasks and who can grant desktop control.
Monitor and log: track which tasks ran, what data was accessed, and how long they took.
Establish stop conditions: timeouts, cost limits, and manual takeover when needed.

Troubleshooting Basics

Model warnings: add or correct your API keys in the environment configuration.
Containers won’t start: check Docker version and logs; ensure required ports are free.
Slow performance: expect longer times for web-heavy tasks; consider simplifying instructions.
No desktop view: verify that desktop access is enabled and the viewer is connected.

Frequently Asked Points

Does it work with local LLMs? Not currently. It expects API-based providers.
Can I deploy it beyond a single machine? Yes, via Kubernetes (Helm) or Railway with an API key.
How does it talk to external tools? Through Model Context Protocol (MCP), which standardizes tool access.
Can I intervene during a run? Yes, you can take control or watch through the live desktop view.

Conclusion

ByteBot brings AI-driven desktop automation into a self-hosted environment. It runs a virtual desktop, accepts natural language requests, and performs real actions across browsers, office tools, and terminals. Setup is straightforward with Docker, and it supports enterprise-friendly deployments, including Kubernetes and Railway.

Be mindful of API-based model costs and what data flows to external providers. If your use case fits repetitive, multi-step workflows across common applications—and you want the option to step in mid-run—ByteBot is worth evaluating for personal and enterprise automation.